The present invention relates to radar detection, and more particularly, to a system and method for adaptive detection of radar targets.
High Frequency Surface Wave Radar (HFSWR) is effective for the continuous detection and tracking of ships, aircraft, icebergs and other surface targets from a shore based location. Accordingly, HFSWR is being used to enhance search and rescue activities as well as to monitor sea state, illegal immigration, drug trafficking, illegal fishing, smuggling and piracy in the Exclusive Economic Zone.
An HFSWR system, installed along a coastal line, comprises a directional transmitting antenna and a receiving antenna array that are directed towards the ocean as well as the hardware and software needed for system operation. The transmitting antenna generates a train of electromagnetic (EM) pulses which illuminate a desired surveillance area. The receiving antenna array should preferably have high and equal gain over the entire surveillance area. Objects in the surveillance area then reflect the EM pulses towards the receiving antenna array which collects radar data. Some of the objects may be elements that must be detected (referred to as xe2x80x9ctargetsxe2x80x9d) while the rest of the objects are elements that do not have to be detected (referred to as xe2x80x9cclutterxe2x80x9d). More sophisticated pulse-coded or frequency-coded EM pulses may be used to combat range-wrap which occurs when a reflected EM pulse (in response to a previously transmitted EM pulse) is received by the receiving antenna array after subsequent EM pulses have been transmitted.
Conventionally, the radar data collected from each antenna element or sensor in the receiving antenna array is then preprocessed by passing the data through a bandpass filter to filter extraneous unwanted signals in the radar data, and then through a heterodyne receiver which demodulates the radar data from the RF band to an IF band where analog to digital conversion occurs. The radar data is then demodulated to the baseband where low-pass filtering and downsampling occurs. The radar data collected by the receiving antenna array is complex (i.e. has real and imaginary components). Accordingly, the downsampled radar data is also complex and each of the signal processing components required to perform the above-mentioned operations are implemented to handle complex data.
The downsampled radar data is then processed by a matched filter that has a transfer function or impulse response that is related to the transmitted EM pulse. The match filtered radar data is then separated into segments for analysis. Each segment is known in the art as a coherent integration time (CIT) or a dwell. The notched filtered radar data in each CIT is range-aligned by noting the time at which each data point was sampled relative to the time that a preceding EM pulse was transmitted. The range-aligned data may then be subjected to a combination of low-pass filtering for further noise reduction and downsampling for more efficient signal processing. The output of this processing is a plurality of time series of range data where each time series is collected for a given range value. The maximum range value for which the plurality of time series is collected depends on the pulse repetition interval used in transmitting the EM pulses (i.e. the frequency at which EM pulses are transmitted).
A target is detected from range, doppler and azimuth information that is generated from the recorded radar data. The range information is used to provide an estimate of the target""s distance from the receiving antenna array. The azimuth information is used to provide an estimate of the angle of the target""s location with respect to the center of the receiving antenna array and the doppler information is used to provide an estimate of the target""s radial velocity by measuring the target""s doppler shift. The target""s doppler shift is related to the change in frequency content of the EM pulse that is reflected by the target with respect to the original frequency content of that EM pulse.
As mentioned previously, range data is generated by noting the time at which data is sampled relative to the time at which a preceding EM pulse is transmitted. Doppler processing corresponds to the detection of a sinusoidal signal of frequency xcex94f at the pulse repetition period (i.e. the time between consecutive transmitted pulses in the coherent pulse train). Accordingly, doppler information is generated for a given range value by subjecting the time series obtained for that range value to comb filter processing, filter bank processing or FFT processing. The azimuth data is conventionally obtained by digital beamforming. More specifically, the radar data at a given range cell and a given doppler cell is weighted by a complex exponential for each antenna element of the receiving antenna array and then summed across all antenna elements. The phase of the complex exponential is related to an azimuth angle, the antenna element spacing and the wavelength of the transmitted EM pulses as is well known to those skilled in the art. Beamforming gives the appearance that the antenna array is tuned to a certain region of the surveillance area defined by the azimuth value used in the complex exponential weights. In this fashion, many beams may be formed to simultaneously cover the entire surveillance area.
To determine a target""s range, azimuth and velocity, a detector processes the generated range, azimuth and doppler information for a given CIT. In general, the detector looks for peaks at a given cell (i.e. a data value or pixel) in a two dimensional plot known as a range-doppler plot. Target detection usually comprises comparing the amplitude in a given cell with the average amplitude in neighboring cells. The detected targets are then forwarded to a plot extractor which filters the detected targets to reject all of those detections that do not conform to the range, doppler and azimuth properties that are expected for a true target. These filtered targets are then forwarded to a tracker which associates successive detections of a given target to form a track for the target. In this fashion, the movement of the target may be tracked throughout the surveillance area.
The detection process is hindered by the addition of noise, which includes the clutter previously mentioned, in each cell. This may result in the missed detection of a target or the false detection of noise as a target. The noise is problematic since there will be a varying noise level in different cells as well as for radar data collected in different CITs, in different sea-state conditions, during different times of day and season and at different locations. The major sources of radar noise include self-interference, such as ocean clutter and ionospheric clutter, and external interference. Self-interference results from the operation of the radar while external interference is independent of the operation of the radar.
Ionospheric clutter, one of the most significant causes of interference, includes EM pulses that reflect off of the earth""s ionosphere and return directly to the radar (i.e. near vertical incidence clutter), and EM pulses that bounce off of the ionosphere, reflect from the ocean and return to the radar along the reverse path (i.e. sky-wave self-interference or range-wrap clutter). In general, ionospheric clutter accumulates in an annular band spanning several range cells, all azimuth cells and most of the ship doppler band. This narrow band of range cells corresponds to the height or multiple heights of the ionospheric layers relative to the HFSWR installation site. Near vertical incidence ionospheric clutter is also characterized as being very strong, isolated in range and smeared in the doppler dimension over many milli-Hertz. During the night, ionospheric clutter is at its highest level due to the disappearance of the D layer and the merging of the F1 and F2 layers. Furthermore, the characteristics of ionospheric clutter vary with season and other environmental parameters so it is not easy to introduce a robust method to suppress ionospheric noise.
The sea surface comprises a number of waves having different wavelengths and amplitudes. Ocean clutter results from EM pulses that are reflected by ocean waves that are harmonics of the radar wavelength. Two large peaks that dominate the ocean clutter are referred to as Bragg lines which appear as two columns of peaks in a range-doppler plot along all range cells at doppler frequencies determined by the radar operating frequency. The Bragg lines can smear radar detection performance at their corresponding doppler frequencies. However, there is also higher order scatter, related to the sea-state, that results in additional peaks and a continuum of ocean clutter between the Bragg lines. This continuum of ocean clutter contains energy that is related to the sea-state (i.e. surface wind speed and duration) and often limits the detection of small, low-speed targets such as ships.
External interference includes co-channel interference, atmospheric interference and impulsive noise. Co-channel interference results from both local and distant users of the HFSWR frequency band, such as television broadcasters. This interference is range independent and occurs at specific doppler ranges. This interference is also highly directive because it originates from spatially correlated point sources. Co-channel interference may be avoided by choosing alternate carrier frequencies for transmitting the EM pulses. However, co-channel interference from distant sources poses a more serious problem since this interference is more random in time and frequency. Furthermore, there is typically greater co-channel interference at night than during the day due to the lack of D layer absorption during the night.
Atmospheric interference is spatially white with a level that varies as a function of frequency, time of day, season and geographical location. For instance, the noise level due to atmospheric interference at the lower end of the HF band, increases about 20 dB during the night in comparison with daytime levels.
Impulsive noise is due to lightning and manifests itself as a sequence of rapid pulses that are randomly distributed in time and have an amplitude with a large dynamic range. Impulsive noise is not spatially white and results from both local and distant storms. Impulsive noise usually occurs throughout the daily operation of an HFSWR system. Impulsive noise has a doppler spread that is relatively short in duration and may resemble a maneuvering target. Impulsive noise results in an increase in the background noise level. The frequency characteristics of impulsive noise change as a function of the intensity of local storm activity.
Spatially non-white external interference and ocean clutter may be successfully reduced by using sophisticated signal processing methods developed by the inventors of the present invention and described in co-pending patent applications filed concurrently herewith, the first application having Ser. No. 10/383,775 and entitled xe2x80x9cSystem and Method For Spectral Generation in Radarxe2x80x9d and the second application having Ser. No. 10/384,203 and entitled xe2x80x9cA Noise Suppression System and Method for Phased-Array Based Systemsxe2x80x9d. However, after applying these signal processing methods, the radar data still contains noise, the majority of which is spatially white noise due mostly to atmospheric interference. As previously mentioned atmospheric interference is problematic since it results in a noise level that is quite variable.
The variability in noise level due to atmospheric interference affects the detection performance of the HFSWR system in several ways. This noise can result in the missed detection of a target, since the target is indistinguishable from the noise, or the false detection of noise as a target. In addition, the detector of the HFSWR system will output a widely varying number of detections in each CIT since the noise level is so variable over time. This has a detrimental effect on the components of the HFSWR system that follow the detector (i.e. the plot extractor and the tracker). In particular, if too many detections are made by the detector then the tracker of the HFSWR system will become overloaded. It is thus preferable to have a detector that provides a relatively constant number of detections in each CIT regardless of the varying noise level due to atmospheric interference.
It is well known in the art to use a Constant False Alarm Rate (CFAR) detector to provide relatively stable detection performance. In general, a conventional CFAR detector estimates the local noise level for a given range-doppler cell and detects a target at the given range-doppler cell when the amplitude of the radar data of the range-doppler cell is larger than the estimated noise level plus a threshold. In a conventional CFAR detector, the value of the threshold is usually constant. However, because the noise level due to atmospheric interference is quite variable and the noise and the target do not add coherently, the amplitude difference between the target and the noise will also vary. Thus, a conventional CFAR detector will not detect targets having a small amplitude if a constant threshold value is used and the noise level increases. Accordingly, there is a need for a detector which provides a threshold level that can be varied with the noise level to provide a constant detection rate across different CIT""s.
Target detection is also challenging due to target properties that vary across targets such as target type (i.e. a ship target, an air target, etc.) and target velocity. For instance, ships provide larger target indications (i.e. a greater number of range-doppler cells) in a range-doppler plot than air targets such as planes. Accordingly, there is a need for a detector that varies detection parameters based on these different target properties.
In one aspect, the present invention is an adaptive detection system for analyzing range-doppler-azimuth data for target detection. The system comprises a threshold calculator that calculates a threshold value that is based on the standard deviation of the range-doppler-azimuth data and a predetermined probability of detection. The system also comprises a detection module in communication with the threshold calculator to receive the threshold value. The detection module calculates an estimated target amplitude and an estimated noise floor amplitude from range-doppler data that is located in a detection window. The detection module detects a target when the difference between the estimated target amplitude and the estimated noise floor amplitude is larger than the threshold value.
In a second aspect, the present invention provides a detection module for analyzing range-doppler data for target detection. The detection module comprises a window generator for generating a detection window having a target region, a guard region surrounding the target region and a main region surrounding the guard region. The detection module also has a signal calculator in communication with the window generator. The signal calculator calculates an estimated target amplitude and an estimated noise floor amplitude. The estimated target amplitude is preferably a range-doppler cell amplitude at the center of the target region and the estimated noise floor amplitude is preferably an average range-doppler cell amplitude in the main region. The detection module also includes a decision module in communication with the signal calculator. The decision module detects a target when the difference between the estimated target amplitude and the estimated noise floor amplitude is larger than a predetermined threshold value.
In another aspect, the present invention provides an adaptive detection method for analyzing range-doppler-azimuth data for target detection. The detection method comprises:
calculating a threshold value based on the standard deviation of the range-doppler-azimuth data and a predetermined probability of detection;
calculating an estimated target amplitude and an estimated noise floor amplitude based on the range-doppler data contained in a detection window; and,
detecting a target when the difference between the estimated target amplitude and the estimated noise floor amplitude is larger than the threshold value.
In a further aspect, the present invention provides a detection method for analyzing range-doppler data for target detection. The detection method comprises:
generating a detection window having a target region, a guard region surrounding the target region and a main region surrounding the guard region;
calculating an estimated target amplitude and an estimated noise floor amplitude, wherein the estimated target amplitude is preferably a range-doppler cell amplitude at the center of the target region and the estimated noise floor amplitude is preferably an average range-doppler cell amplitude in the main region; and,
detecting a target when the difference between the estimated target amplitude and the estimated noise floor amplitude is larger than a predetermined threshold value;.